<p>This study addresses a critical gap in the literature on sustainability transitions in developing economies by empirically validating the Quadruple Helix (QH) model, encompassing government, industry, academia, and civil society, as a framework for environmental outcomes, with a specific focus on technology transfer as the central mediating mechanism. The QH model is theorized to drive innovation-led sustainable development, but its environmental applicability across heterogeneous regions remains under-explored. Focusing on technology transfer as the central mechanism, the authors investigate how the economic, social, and knowledge dimensions of provincial innovation ecosystems in China interact to shape environmental performance, arguing that regional disparities are fundamentally differences in technology absorption and diffusion capacity. Addressing the oversight of regional heterogeneity, the study employs a five-step regression analysis to test whether these dynamics diverge between industrialized and less-developed provinces. The findings confirm that environmental pressures are proportionate to economic development levels. The authors demonstrate that pathways to improved environmental quality through industrial innovation, social infrastructure, and education are moderated by a territory’s capacity for technology transfer. The adoption of advanced technologies, including AI-driven industrial optimization, blockchain for supply chain transparency, and IoT-based emissions monitoring, is identified as a key mediator of the QH model’s efficacy. The proposed methodology offers policymakers a tool to master the interdependencies between environmental, economic, social, and innovative parameters, with a particular focus on diagnosing and strengthening local technology transfer systems for holistic, territory-specific strategies. The authors conclude that effective green transitions require a knowledge-based convergence of all QH dimensions, centered on enhancing absorptive capacity and effective, demand-driven technology diffusion, not merely innovation output. Future extensions of this framework could leverage AI analytics and Web3 technologies, such as decentralized autonomous organizations (DAOs), to reconfigure stakeholder coordination and governance specifically to track and facilitate the flow of technologies from creators to adopters. By reducing reliance on centralized intermediaries and enabling programmable, trust-minimized collaboration, DAOs can optimize the co-creation, diffusion, and localized adoption of green solutions within dynamic innovation ecosystems, thereby providing more direct metrics for successful technology transfer beyond patent counts. This study situates the QH model within China’s institutional landscape, where robust state capacity, centralized governance, and strategic industrial policy play defining roles. The results confirm the model’s utility as a conduit for sustainability shifts under these conditions, while highlighting its limits in contexts defined by decentralized authority, an independent civil society, or market-led technology transfer. By providing a regionally embedded analysis of how innovation ecosystems shape environmental progress, the paper adds a perspective particularly pertinent to other developing economies with comparable structures.</p>